Python cbook.is_scalar方法代碼示例

# 需要導入模塊: from matplotlib import cbook [as 別名]
# 或者: from matplotlib.cbook import is_scalar [as 別名]
def less_simple_linear_interpolation( x, y, xi, extrap=False ):
    """
    This function provides simple (but somewhat less so than
    :func:`cbook.simple_linear_interpolation`) linear interpolation.
    :func:`simple_linear_interpolation` will give a list of point
    between a start and an end, while this does true linear
    interpolation at an arbitrary set of points.

    This is very inefficient linear interpolation meant to be used
    only for a small number of points in relatively non-intensive use
    cases.  For real linear interpolation, use scipy.
    """
    if cbook.is_scalar(xi): xi = [xi]

    x = np.asarray(x)
    y = np.asarray(y)
    xi = np.asarray(xi)

    s = list(y.shape)
    s[0] = len(xi)
    yi = np.tile( np.nan, s )

    for ii,xx in enumerate(xi):
        bb = x == xx
        if np.any(bb):
            jj, = np.nonzero(bb)
            yi[ii] = y[jj[0]]
        elif xx<x[0]:
            if extrap:
                yi[ii] = y[0]
        elif xx>x[-1]:
            if extrap:
                yi[ii] = y[-1]
        else:
            jj, = np.nonzero(x<xx)
            jj = max(jj)

            yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj])

    return yi 

# 需要導入模塊: from matplotlib import cbook [as 別名]
# 或者: from matplotlib.cbook import is_scalar [as 別名]
def plot(self, xs, ys, *args, **kwargs):
        '''
        Plot 2D or 3D data.

        ==========  ================================================
        Argument    Description
        ==========  ================================================
        *xs*, *ys*  X, y coordinates of vertices

        *zs*        z value(s), either one for all points or one for
                    each point.
        *zdir*      Which direction to use as z ('x', 'y' or 'z')
                    when plotting a 2D set.
        ==========  ================================================

        Other arguments are passed on to
        :func:`~matplotlib.axes.Axes.plot`
        '''
        # FIXME: This argument parsing might be better handled
        #        when we set later versions of python for
        #        minimum requirements.  Currently at 2.4.
        #        Note that some of the reason for the current difficulty
        #        is caused by the fact that we want to insert a new
        #        (semi-optional) positional argument 'Z' right before
        #        many other traditional positional arguments occur
        #        such as the color, linestyle and/or marker.
        had_data = self.has_data()
        zs = kwargs.pop('zs', 0)
        zdir = kwargs.pop('zdir', 'z')

        argsi = 0
        # First argument is array of zs
        if len(args) > 0 and cbook.iterable(args[0]) and \
                len(xs) == len(args[0]) :
            # So, we know that it is an array with
            # first dimension the same as xs.
            # Next, check to see if the data contained
            # therein (if any) is scalar (and not another array).
            if len(args[0]) == 0 or cbook.is_scalar(args[0][0]) :
                zs = args[argsi]
                argsi += 1

        # First argument is z value
        elif len(args) > 0 and cbook.is_scalar(args[0]):
            zs = args[argsi]
            argsi += 1

        # Match length
        if not cbook.iterable(zs):
            zs = np.ones(len(xs)) * zs

        lines = Axes.plot(self, xs, ys, *args[argsi:], **kwargs)
        for line in lines:
            art3d.line_2d_to_3d(line, zs=zs, zdir=zdir)

        self.auto_scale_xyz(xs, ys, zs, had_data)
        return lines